Compared to the first type of methods, the second category aims to tackle a newly proposed problem, which has not been sufficiently studied in the literature and remains an open yet challenging issue. To tackle this issue, SAT [1]}, makes the first attempt by introducing a distribution consistency assumption between attribute and structure embedding sub-networks to guide the generation of more meaningful latent embedding.
Though demonstrating high quality of attribute restoration in various downstream tasks, SAT suffers from the following non-negligible limitations: 1) adopts two decoupled sub-networks for information extraction, thus isolates the learning of attribute and structure embedding; 2) imposes too strict distribution assumption on the latent variables, which largely decreases the discriminative capability of the learned representation; 3) it lacks a structure-attribute information filtering and refining mechanism for data completion, resulting in less robust feature representations.
